Incorporating support vector machines with multiple criteria decision making for financial crisis analysis

Feature selection is an essential pre-processing technique in data mining that eliminates redundant or unrepresentative attributes and improves the performance of classifiers. However, a classifier with different feature selection approaches results in diverse outcomes. Thus, determining how to integrate feature selection methods and yield an appropriate feature set is an issue worth further study. Based on ensemble learning, this investigation develops a SVMMCDM (support vector machines with multiple criteria decision making) model that employs various feature selection techniques as data preprocessing schemes and then uses SVM for financial crisis prediction. The study uses MCDM to determine the most suitable feature selection mechanism when many performance criteria are considered. After the feature selection mechanism has been determined, the study decomposes the SVM to obtain support vectors and predicted labels which are then fed into a decision tree to generate rules. The numerical results for the ex-ante and ex-post periods relative to the financial tsunami show that the proposed SVMMCDM model is an effective way to predict a financial crisis and can provide useful rules for decision makers.     

混流生产的属性层次模型分析

首先介绍了程乾生教授的属性层次模型方法,然后以某车桥公司装配车间实施的混流生产为例,利用属性层次模型建立了关于混流生产的系统评价模型,并论证了该方法的可行性。     

基于区间型不确定多属性决策的地下储气库设计方案优选

地下储气库的设计方案涉及因素多,必须对方案从整体上进行综合评价,建立正确的决策模型,从而客观合理确定优选方案。结合地下储气库设计方案优选的多属性、不确定性和群决策等特征,运用层次分析法赋予指标权重,并构建了地下储气库设计方案优选的区间型不确定多属性决策模型。实例表明,该方法切实可行,操作简便,为地下储气库设计方案优选提供了新的思路。     

Predicting the failures of prediction markets: A procedure of decision making using classification models

Prediction markets have been an important source of information for decision makers due to their high ex post accuracies. Nevertheless, recent failures of prediction markets remind us of the importance of ex ante assessments of their prediction accuracy. This paper proposes a systematic procedure for decision makers to acquire prediction models which may be used to predict the correctness of winner-take-all markets. We commence with a set of classification models and generate combined models following various rules. We also create artificial records in the training datasets to overcome the imbalanced data issue in classification problems. These models are then empirically trained and tested with a large dataset to see which may best be used to predict the failures of prediction markets. We find that no model can universally outperform others in terms of different performance measures. Despite this, we clearly demonstrate a result of capable models for decision makers based on different decision goals.     

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.     

The attributes diagram A geometrical framework for assessing the quality of probability forecasts

In two-event situations, a reliability diagram provides a geometrical framework for evaluating this attribute of probability forecasts. However, reliability is only one of several important attributes of such forecasts. This paper describes an extension of the reliability diagram - the attributes diagram - in which the accuracy, resolution, and skill, as well as the reliability, of probability forecasts can be depicted. Moreover, these geometrical representations are shown to be directly related to quantitative measures of the respective attributes. The interpretation and use of the attributes diagram is illustrated by considering samples of probabilistic quantitative precipitation forecasts. Some possible extensions of this diagram to multiple-event situations are briefly discussed.     

基于属性识别模型的边坡稳定性评价

结合工程实例,将属性识别模型应用于路堑边坡稳定性评价,提出了路堑边坡稳定性评价的属性测度计算方法并利用Shannon熵理论确定权系数,建立了基于熵权的属性识别模型。实例评价结果与计算结果一致,证实了该方法的可行性和有效性。此方法结果合理,可以有效地解决边坡稳定性评价问题。     

群体决策复合准则在供应商选择中的应用

供应商多属性的本质是供应商选择问题复杂性的主要原因之一,特别是属性为描述性质的模糊数时,有限理性的个体在决策制定过程中受到很大的局限。实际操作过程中,专家评分法运用广泛,在此基础上,文中将其与群决策理论相结合,首先指出了对供应商进行评价时常出现的平局问题,将群体决策复合准则引入到供应商选择过程中,然后建立模型,对其求解,最后结合算例,说明该方法的有效性。     

Estimating house price appreciation: A comparison of methods

Several parametric and nonparametric methods have been advanced over the years for estimating house price appreciation. This paper compares five of these methods in terms of predictive accuracy, using data from Montgomery County, Pennsylvania. The methods are evaluated on the basis of the mean squared prediction error and the mean absolute prediction error. A statistic developed by Diebold and Mariano is used to determine whether differences in prediction errors are statistically significant. We use the same statistic to determine the effect of sample size on the accuracy of the predictions. In general, parametric methods of estimation produce more accurate estimates of house price appreciation than nonparametric methods. And when the mean absolute prediction error is used as the criterion of accuracy, the repeat sales method produces the most accurate estimate among the parametric methods we tested. Finally, of the five methods we tested, the accuracy of the repeat sales method is least diminished by a reduction in sample size.     

基于粗集属性重要性的供应链信息共享的模糊综合评价

供应链信息共享是供应链渠道协调的重要方法，信息共享的程度评价为供应链节点企业的信息共享激励决策以及合作伙伴关系的评估提供重要依据。文章提出了信息共享的评价指标体系．利用评价指标属性的重要性作为权重值，解决了确定权重值的主观性问题。利用粗糙集理论结合模糊数学的有关理论建立了供应链信息共享的综合评价模型并进行了算例分析，为节点企业的激励决策和合作伙伴关系的评价提供依据和理论指导。     

Discovering medical quality of total hip arthroplasty by rough set classifier with imbalanced class

The incidence of THA (total hip arthroplasty) will rise with an aging population and improvements in surgery, a feasible alternative in health care can effectively increase medical quality. The reason of a hip joint replaced is to relieve severe arthritis pain that is limiting your activities. Hip joint replacement is usually done in people age 60 and older. Younger people who have a hip replaced may put extra stress on the artificial hip. This paper uses a serious data screening function by experts to reduce data dimension after data collection from the National Health Insurance database. The proposed model also adopts an imbalanced sampling method to solve class imbalance problem, and utilizes rough set theory to find out core attributes (selected 7 features). Based on the core attributes, the extracted rules can be comprehensive for the rules of medical quality. In verification, THA dataset is taken as case study; the performance of the proposed model is verified and compared with other data-mining methods under various criteria. Furthermore, the performance of the proposed model is identified as winning the listing methods, as well as using hybrid-sampling can increase the far true-positive rate (minority class). The results show that the proposed model is efficient; the performance is superior to the listing methods under the listing criteria. And the generated decision rules and core attributes could find more managerial implication. Moreover, the result can provide stakeholders with useful THA information to help make decision.     

条件固定效应回归的活动目的地选择模型——一个案例研究

本文以城市居民出行目的地选择为研究对象,建立条件固定效应回归（C—Logit）目的地选择模型。结合我国某城市交通小区城市居民日出行数据对模型进行标定求解,并描述目的地选择预测流程,解释了影响居民出行目的地选择的诸多因素。对预测结果进行分析,得到较高的目的地选择预测精度,为从非集计层面研究城市居民出行目的地选择提供了新的思路。     

The assessment of the expected value of information in the binary decision model

The expected value of information represents the maximum amount the decision maker should spend on inquiry before making a decision. This amount depends upon the accuracy of the information. In many cases of inquiry, prior objective knowledge of the accuracy is not available. This paper presents and compares two methods of subjectively assessing the value of imperfect information in the binary decision model. In the first method, the decision maker provides a likelihood function for the inquiry and hence the probabilities of error. The second method is the preposterior approach, in which the decision maker provides the prior distribution for the posterior probability.     

Bayesian portfolio selection using a multifactor model

This article develops a new portfolio selection method using Bayesian theory. The proposed method accounts for the uncertainties in estimation parameters and the model specification itself, both of which are ignored by the standard mean-variance method. The critical issue in constructing an appropriate predictive distribution for asset returns is evaluating the goodness of individual factors and models. This problem is investigated from a statistical point of view; we propose using the Bayesian predictive information criterion. Two Bayesian methods and the standard mean-variance method are compared through Monte Carlo simulations and in a real financial data set. The Bayesian methods perform very well compared to the standard mean-variance method.     

Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming

Global supplier selection is a multi-goal multi-criteria problem which needs to consider both qualitative and quantitative factors. Which suppliers are the best and how much should be purchased from the selected suppliers is an important purchasing issue for manufacturers. Traditionally, decision makers can determine the best supplier from evaluating few suppliers with qualitative supplier selection criteria by using fuzzy analytic hierarchy process (FAHP), but evaluate dozens of global suppliers simultaneously or determine the order quantity from them will be complex and difficult. Meanwhile, decision makers can determine the order quantity form the suitable suppliers by using fuzzy goal programming (FGP); however, it is not easy to decide weights for each goal of global supplier selection with different supply chain strategies. This study integrated the FAHP and FGP (FAHP-FGP) method to be a new approach for global supplier selection in considering the manufacturer’s supply chain strategies. With FAHP-FGP method, the manufacturer can consistently integrate multi-manager’ opinions in determining weights of each goal and obtain the order quantities for suitable suppliers based on manufacturer’s strategies. To demonstrate the usefulness of the proposed method, a real-world case of a digital consumer products manufacturer is presented.     

基于属性论的供应链管理中供应商的综合评价与优化选择

分析了供应链管理中供应商评估选择的含义及特点,构建了供应商的评估选择的指标体系,提出基于属性论的供应商的评估选择的数学模型,并给出了模拟的评判界面,旨在提高整条供应链的运作绩效。     

贝叶斯非线性分层模型在多元索赔准备金评估中的应用

本文将贝叶斯非线性分层模型应用于基于不同业务线的多元索赔准备金评估中,设计了一种合适的模型结构,将非线性分层模型与贝叶斯方法结合起来,应用WinBUGS软件对精算实务中经典流量三角形数据进行建模分析,并使用MCMC方法得到了索赔准备金完整的预测分布。这种方法扩展并超越了已有多元评估方法中最佳估计和预测均方误差估计的研究范畴。在贝叶斯框架下结合后验分布实施推断对非寿险公司偿付能力监管和行业决策具有重要作用。     

A kind of new time-weighted nonnegative lasso index-tracking model and its application

This study describes improved index-tracking methods to replicate the target index’s market performance in a high-dimensional sparse linear regression with nonnegative constraints on the coefficients. The main objective of this study is to construct a sparse portfolio with a better prediction effect and robustness. Considering the influence of time factors on index tracking, we propose a time-weighted nonnegative lasso index tracking model under different market constraints and define two new time-weighted construction methods. This index tracking model is an extension of Lasso and has variable selection consistency and estimation consistency under time-weighted nonnegative irrepresentable conditions similar to the irrepresentable condition in Lasso. We use the multiplicative updates algorithm to obtain the model’s solution since it is faster and simpler. The constrained index tracking problem in the stock market without short sales is studied in the latter part. The empirical results indicate that the optimized time-weighted nonnegative lasso index tracking model can obtain a smaller out-of-sample tracking error. The constructed portfolio has a better prediction effect and robustness, and we find that the exponential time-weighted method is better than the linear time-weighted method in capturing time information.     

Bayesian Estimation of Prediction Error and Variable Selection in Linear Regression

An important statistical application is the problem of determining an appropriate set of input variables for modelling a response variable. In such an application, candidate models are characterized by which input variables are included in the mean structure. A reasonable approach to gauging the propriety of a candidate model is to define a discrepancy function through the prediction error associated with this model. An optimal set of input variables is then determined by searching for the candidate model that minimizes the prediction error. In this paper, we focus on a Bayesian approach to estimating a discrepancy function based on prediction error in linear regression. It is shown how this approach provides an informative method for quantifying model selection uncertainty.